A brain network model for depression: From symptom understanding to disease intervention

Abstract

Understanding the neural substrates of depression is crucial for diagnosis and treatment. Here, we review recent studies of functional and effective connectivity in depression, in terms of functional integration in the brain. Findings from these studies, including our own, point to the involvement of at least four networks in patients with depression. Elevated connectivity of a ventral limbic affective network appears to be associated with excessive negative mood (dysphoria) in the patients; decreased connectivity of a frontal-striatal reward network has been suggested to account for loss of interest, motivation, and pleasure (anhedonia); enhanced default mode network connectivity seems to be associated with depressive rumination; and diminished connectivity of a dorsal cognitive control network is thought to underlie cognitive deficits especially ineffective top-down control of negative thoughts and emotions in depressed patients. Moreover, the restoration of connectivity of these networks-and corresponding symptom improvement-following antidepressant treatment (including medication, psychotherapy, and brain stimulation techniques) serves as evidence for the crucial role of these networks in the pathophysiology of depression.

Keywords: affective network; cognitive control network; default mode network; depression; reward network.

© 2018 John Wiley & Sons Ltd.

Figures

Figure 1

Characterization of different approaches to examine brain connectivity. Experimental inputs usually enter into sensory cortex and cause changes in neuronal activity X1 in the region (R1). Activity in R1 will then be propagated to a second region R2 which is connected to R1 and causes changes in X2. The neuronal activity X1 and X2 are hidden neuronal states because they cannot be observed directly using fMRI. Instead, the BOLD signals recorded in fMRI images are a convolution of the neuronal states with a hemodynamic function. Functional connectivity analyses simply measure the undirected temporal correlations (or statistical dependencies) among observed BOLD signals of different brain regions. Granger causality modeling (GCM) tries to infer directed connectivity using autoregressive models. Strictly speaking, GCM measures directed functional connectivity because it operates on observed hemodynamic (BOLD) responses. In contrast, dynamic causal modeling (DCM) estimates the influence that the neural activity of one brain region exerts on another. FC: functional connectivity; EC: effective connectivity

Figure 2

Dysconnectivity and depression. Four networks including the affective network (AN), reward network (RN), default mode network (DMN), and cognitive control network (CCN) have been mainly associated with the neural substrates of depression, with hyperconnectivity (marked in red) of the AN and DMN and attenuated connectivity (marked in green) of the RN and CCN observed in the patients. OFC: orbitofrontal cortex; INS: insula; AMY: amygdala; HIP: hippocampus; vACC: ventral anterior cingulate cortex; mPFC: medial prefrontal cortex; PCC: posterior cingulate cortex; PCUN: precuneus; ANG: Angular; DLPFC: dorsolateral prefrontal cortex; dACC: dorsal anterior cingulate cortex; PFC: prefrontal cortex; CAU: caudate; NA: nucleus accumbens. This figure was prepared with the BrainNet Viewer132

Figure 3

Brain effects of antidepressant treatment. A large part of aberrant connections reported in the patients have been shown to be normalized after treatment with antidepressants, psychotherapy, repetitive transcranial magnetic stimulation (rTMS), deep brain stimulation (DBS), and electroconvulsive therapy (ECT). This figure was prepared with the BrainNet Viewer132